One of the biggest challenges in retail operations is and always has been getting to know the customer. That was an easier task to accomplish before megastores, strip malls, the Web, and multichannel contact centers. Back in the days of local markets and small corner stores, customers and inventories could be managed easily on index cards and handwritten ledgers.
But today, large and small retailers alike are searching for methods and systems to identify what was bought when and by whom. Simply scaling-up older systems, like the index or ledger, will not work when the business model has changed as drastically as it has in the past 50 years. That can be a tremendous challenge, but the payoff of collecting customer information makes it worthwhile.
By knowing and understanding customers and their habits, marketers, management, and operational departments can analyze and optimize efforts ranging from pricing models, inventory, or shelving configurations to store locations and marketing communications. Before companies can enjoy the benefits of customer data application in any CRM strategy or system, they first need to find that key data: personally identifying information.
Basically, there are two types of customers: first-time buyers and returning customers. Most organizations do not differentiate. All customers look the same unless active measures are put in place to identify new from old and high value from high maintenance.
With approximately 116 million households and 276 million people in the United states, there are no silver bullets in the battle to identify customers, as there are no single universal identifiers readily available to make all consumers unique across the board. Information that comes close, such as social security, driver's license, or bank account numbers are not readily provided in the retail environment due to security and privacy concerns, among other reasons. A flexible system utilizing multiple pieces of information to identify individuals is now- -and will continue to be- -the retailer's best resource for completing accurate customer profiles over multiple channels.

Data Collection
The first step in building a customer database is to determine what information will serve as an individually identifying key. That ID will act as the key to storing and retrieving additional information such as customer and transaction history, analysis, and marketing communications.
Types of identifying information that retailers can collect include name, address, or one of many numeric identifiers, such as bankcard, phone, or checking account numbers. Returning customers can be easier to identify through usage of account, branded store card, personalized coupons, or loyalty program numbers.
Deciding what to collect is often easier than actually collecting the information. Web and contact center channels, which depend on phone number or log-in and password combinations to identify discrete individuals, rate in the 90 percent to 100 percent range in their ability to collect the key data necessary to identify individuals. While that is the goal, the challenge comes in reflecting their type of success in all channels consistently. The main barrier to building a multichannel profile of customers lies in the main purchase channel: the retail store.
There are two methods for collecting identifying information: explicit and implicit inference. Explicit collection includes data entry or scans of personally identifying information such as those listed above. Information such as a Zip code can be collected quickly and can be used to provide a wealth of inferred information. While that does not focus on the individual, it is a good first step for those retailers not collecting any information today.
The most efficient means is to request and collect personally identifying information at the point of sale (POS). This process can be affected by the value proposition offered in exchange for consumers' identities. Depending on the POS configuration, the explicit collection process can be made seamless to the customer by recording information typically used in the transaction, such as a bankcard or checking account number. That serves as a robust additional internal match key, but no longer as a source of external matching services.
While most matching and appending of retail customer data traditionally has been performed by back-end batching and processing, technology is now available that allows this to occur in real or near-real time at the front end of the transaction. Additionally, real-time processing opens the door to more relevant messaging, delivery of personalized offers, and the ability to update or collect missing customer profile elements such as name, phone number, and home, work, or e-mail addresses.
Historically, retailers would use a process called reverse bankcard appending (RBCA) to identify customers personally. Credit card numbers would be captured from polled transactions, batched, and sent to a third-party processing company. Matched records would be sent back to the retailer. This method of identification was controversial due to the sensitivity of credit information that can be attached to consumers through their tender. Ongoing privacy legislation brought the system under legislative scrutiny, causing the major service providers to discontinue it as a service offering.
Once the account is linked to a customer name and address, RBCA can be effective for identifying up to 60 percent of returning customer transactions. Cash transactions are the exception to this type of seamless identification. As cash does not come with a bank account number, other information is required to match these transactions. Bankcard number matching can be favorable as an incremental identification process, but not as a first-line method of identification.
Transparent collection can occur by scanning personalized coupons or loyalty program cards during the transaction. While that is fine for identifying existing customers, it will not capture information on new ones. Additionally, members may not elect to identify themselves in small transactions due to the negligible benefit to the program. Membership program enrollment rates can range from a fraction of a percent to 80 percent of the total customer base. Due to the inability to capture new customers and existing customers who elect not to enroll, this identification method is most effective when combined with other methods.
The most obvious, common, and potentially intrusive process is to request information verbally for data entry during the transaction. Often retailers opt for implied information in this case, which can be less invasive to the transaction process.
Of the unique identifying data elements listed above, address often takes too much time to collect in a busy retail environment and spelling variations can limit proper matching. But name and Zip code combinations can reduce the duplication.
A phone number is the most time-efficient piece of information to collect. While most consumers have business, home, mobile, and fax numbers, this 10-digit number can quickly narrow the search to the household level, if not the individual. With match rates similar to RBCA, the phone number is a viable replacement for the disappearing service. The drawbacks are similar to other processes. For example, home phone numbers are usually associated with households, not individuals, and also are subject to change.
The most effective way for retailers to capture customer identifiers will continue to be a multistep process and can be used successfully to unlock a treasure trove of information until universal identifiers become commonplace.
Creative strategy
Through technological developments, the tools to manage customer information are available to the retailer. POS-based systems can be used to draw from and feed data warehouses in real time. Technology can help build robust stores of data, as well as expedite the usage of information for analysis and even management of multichannel marketing, such as combination mail, e-mail, and retail messaging campaigns.
Smart marketing can make the retailer's job easier and encourage self-identification. A good loyalty or frequency program with tangible rewards will have at least a portion of the customer base clamoring to make sure their every transaction is identified. Remember that part of the value of a good program will come from relevance, which can only be derived from customer knowledge gathered from complete, accurate customer data.
Retailers should have routine training in place to reinforce policies and ensure that those in the position of power- -the cashier- -know how to use the technology to the fullest. That will provide a strong basis for marketing, management, and operational analysis. Often it is the business process that needs to be reengineered to properly capture customer data. If the proper processes are not in place, the entire operation can be affected adversely.
In order to survive and thrive, savvy retailers will do whatever it takes to know the customer. Building a knowledge base, implementing CRM strategies, and communicating with the customer are the keys to developing and maintaining a competitive edge in today's economy. By understanding customers, retailers can differentiate themselves from the competition by simply offering relevance, understanding the customer's needs, and- -most of all- -providing a great experience.
Four steps to Gathering Quality Data
Collecting customer data in the retail environment can be a challenge. The best approach is to use a multistep strategy that includes:
- Requesting such information as Zip code or phone number at the point of sale.
- Using transparent collection methods such as swiping loyalty cards.
- Educating cashiers on the best strategies for encouraging customers to give personally identifying information or to use their loyalty cards for every purchase, no matter how small.
- Reward customers for giving personally identifying information. For example, offer discounts or points for every time a customer uses a loyalty card when making a purchase.